Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/62566
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dc.contributor.authorXu, Zhengzi
dc.date.accessioned2015-04-20T08:35:46Z
dc.date.available2015-04-20T08:35:46Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10356/62566
dc.description.abstractWith the Android mobile device becoming increasingly popular, the Android application market has become a main target of the malware attacks. Therefore, many methods have been used to protect the mobile application users from being attacked. However, those methods have shortcomings in detecting the malware within a short time, and can be easily bypassed. To detect the malware before the installed time, and overcome the drawbacks of dynamic analysis and signature based analysis, the machine learning based malware detection methods has been proposed. In this project, I have adopted this approach to develop a tool to extract Android application features, and built the classification model using the generated feature sets. The result shows that classification the model can reach 98% accuracy in predicting the maliciousness of the application. I have also generated the transformation attack samples, which will be used in further machine learning based malware detection studies.en_US
dc.format.extent41 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleMachine learning methods for Android malware detectionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLiu Yangen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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